Implementations described herein generally relate to the prediction of user traffic for online sites.
In an increasingly networked world, consumers frequently use online resources to access information. For example, search engines are a popular tool through which users enter a search query describing information of interest and receive back documents or links to documents that relate to the search query. Search engines may be tailored to a particular area of interest, such as a general web search engine, a news search engine, or a shopping search engine.
It may be desirable for an operator of an online resource, such as a search engine, to be able to intelligently predict information about the type of users that are likely to visit a particular web site. This type of “traffic prediction” can be used, for example, when the search engine displays advertisements that link to a particular web site. If the search engine provider knows a general profile that characterizes traffic that is likely to click on a particular advertisement or visit the web site referenced by the advertisement, the search engine provider may be able to spot invalid or non-genuine user activity that is not reflective of true user interest. Distinguishing invalid user activity (i.e., traffic) from genuine user activity can be particularly important when, for example, the search engine provider charges advertisers based on the traffic that is referred to the advertiser's web site. In this situation, the search engine provider may like to be able to distinguish invalid user activity from genuine user activity and only charge the advertiser for the genuine user activity.
Predicting user activity can be useful in a number of online contexts in addition to the display of advertisements and outside of the context oif a search engine. For example, a web site designer may wish to predict how a proposed change to a web site will affect user activity at or to the web site.